arXiv subjects

Guilhem Lavaux

Publications and source records attributed to Guilhem Lavaux.

At least 55 records · Page 3Linked to original sources

arXiv2022

Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys

Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of next-generation galaxy surveys. The signal will permit us to determine primordial physics processes and constrain models of cosmic inflation. While traditional approaches utilise a limited set of statistical summaries of the galaxy distribution to constrain primordial non-Gaussianity, we present a field-level approach by Bayesian forward-modelling the entire three-dimensional galaxy survey. Our method naturally and fully self-consistently exploits the entirety of the large-scale structure, e.g., higher-order statistics, peculiar velocity fields, and scale-dependent galaxy bias, to extract information on the local non-Gaussianity parameter, $\fnl$. We demonstrate the performance of our approach through various tests with mock galaxy data emulating relevant features of the \sdssiii{}-like survey, and additional tests with a \textit{Stage IV} mock data set. These tests reveal that the method infers unbiased values of $\fnl$ by accurately handling survey geometries, noise, and unknown galaxy biases. We demonstrate that our method can achieve constraints of $σ_{\fnl} \approx 8.78$ for \sdssiii{}-like data, an improvement of a factor $\sim 2.5$ over currently published constraints. Tests with next-generation mock data show that significant further improvements are feasible with sufficiently high resolution. Furthermore, the results demonstrate that our method can consistently marginalise all nuisance parameters of the data model. The method further provides an inference of the three-dimensional primordial density field, providing opportunities to explore additional signatures of primordial physics.

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arXiv2022

SIBELIUS-DARK: a galaxy catalogue of the Local Volume from a constrained realisation simulation

We present SIBELIUS-DARK, a constrained realisation simulation of the local volume to a distance of 200~Mpc from the Milky Way. SIBELIUS-DARK is the first study of the \textit{Simulations Beyond The Local Universe} (SIBELIUS) project, which has the goal of embedding a model Local Group-like system within the correct cosmic environment. The simulation is dark-matter-only, with the galaxy population calculated using the semi-analytic model of galaxy formation, GALFORM. We demonstrate that the large-scale structure that emerges from the SIBELIUS constrained initial conditions matches well the observational data. The inferred galaxy population of SIBELIUS-DARK also match well the observational data, both statistically for the whole volume and on an object-by-object basis for the most massive clusters. For example, the $K$-band number counts across the whole sky, and when divided between the northern and southern Galactic hemispheres, are well reproduced by SIBELIUS-DARK. We find that the local volume is somewhat unusual in the wider context of $Λ$CDM: it contains an abnormally high number of supermassive clusters, as well as an overall large-scale underdensity at the level of $\approx 5$\% relative to the cosmic mean. However, whilst rare, the extent of these peculiarities does not significantly challenge the $Λ$CDM model. SIBELIUS-DARK is the most comprehensive constrained realisation simulation of the local volume to date, and with this paper we publicly release the halo and galaxy catalogues at $z=0$, which we hope will be useful to the wider astronomy community.

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arXiv2021

Optimal machine-driven acquisition of future cosmological data

We present maps classifying regions of the sky according to their information gain potential as quantified by the Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculate the response of observed cosmic structures to perturbative changes in the cosmological model and chart their respective contributions to the Fisher information. Our physical forward modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We demonstrate this using galaxy counts data and showcase the potential of our approach by studying the information gain of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative about our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind and elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward to perform efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.

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arXiv2021

Reconstructing dark matter distribution with peculiar velocities: Bayesian forward modelling with corrections for inhomogeneous Malmquist bias

We present a forward-modelled velocity field reconstruction algorithm that performs the reconstruction of the mass density field using only peculiar velocity data. Our method consistently accounts for the inhomogeneous Malmquist bias using analytic integration along the line-of-sight. By testing our method on a simulation, we show that our method gives an unbiased reconstruction of the velocity field. We show that not accounting for the inhomogeneous Malmquist bias can lead to significant biases in the forward-modelled reconstructions. We applied our method to a peculiar velocity data set consisting of the SFI++ and 2MTF Tully-Fisher catalogues and the A2 supernovae compilation, thus obtaining a novel velocity reconstruction in the local Universe. Our velocity reconstructions have a cosmological power spectrum consistent with the theoretical expectation. Furthermore, we obtain a full description of the uncertainties on reconstruction through samples of the posterior distribution. We validate our velocity reconstruction of the local Universe by comparing it to an independent reconstruction using the 2M++ galaxy catalogue, obtaining good agreement between the two reconstructions. Using Bayesian model comparison, we find that our velocity model performs better than the adaptive kernel smoothed velocity with the same peculiar velocity data. However, our velocity model does not perform as well as the velocity reconstruction from the 2M++ galaxy catalogue, due to the sparse and noisy nature of the peculiar velocity tracer samples. The method presented here provides a way to include peculiar velocity data in initial condition reconstruction frameworks.

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arXiv2021

Detectability of large correlation length inflationary magnetic field with Cherenkov telescopes

Magnetic fields occupying the voids of the large scale structure may be a relic from the Early Universe originating from either Inflation or from cosmological phase transitions. We explore the possibility of identifying the inflationary origin of the void magnetic fields and measuring its parameters with gamma-ray astronomy methods. The large correlation length inflationary field is expected to impose a characteristic asymmetry of extended gamma-ray emission that is correlated between different sources on the sky. We show that a set of nearby blazars for which the extended emission is observable in the 0.1-1 TeV band with CTA can be used for the test of inflationary origin of the void magnetic fields.

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arXiv2021

Lifting weak lensing degeneracies with a field-based likelihood

We present a field-based approach to the analysis of cosmic shear data to infer jointly cosmological parameters and the dark matter distribution. This forward modelling approach samples the cosmological parameters and the initial matter fluctuations, using a physical gravity model to link the primordial fluctuations to the non-linear matter distribution. Cosmological parameters are sampled and updated consistently through the forward model, varying (1) the initial matter power spectrum, (2) the geometry through the distance-redshift relationship, and (3) the growth of structure and light-cone effects. Our approach extracts more information from the data than methods based on two-point statistics. We find that this field-based approach lifts the strong degeneracy between the cosmological matter density, $Ω_\mathrm{m}$, and the fluctuation amplitude, $σ_8$, providing tight constraints on these parameters from weak lensing data alone. In the simulated four-bin tomographic experiment we consider, the field-based likelihood yields marginal uncertainties on $σ_8$ and $Ω_\mathrm{m}$ that are, respectively, a factor of 3 and 5 smaller than those from a two-point power spectrum analysis applied to the same underlying data.

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arXiv2021

Bayesian cosmological inference through implicit cross-correlation statistics

Analyzes of next-generation galaxy data require accurate treatment of systematic effects such as the bias between observed galaxies and the underlying matter density field. However, proposed models of the phenomenon are either numerically expensive or too inaccurate to achieve unbiased inferences of cosmological parameters even at mildly-nonlinear scales of the data. As an alternative to constructing accurate galaxy bias models, requiring understanding galaxy formation, we propose to construct likelihood distributions for Bayesian forward modeling approaches that are insensitive to linear, scale-dependent bias and provide robustness against model misspecification. We use maximum entropy arguments to construct likelihood distributions designed to account only for correlations between data and inferred quantities. By design these correlations are insensitive to linear galaxy biasing relations, providing the desired robustness. The method is implemented and tested within a Markov Chain Monte Carlo approach. The method is assessed using a halo mock catalog based on standard full, cosmological, N-body simulations. We obtain unbiased and tight constraints on cosmological parameters exploiting only linear cross-correlation rates for $k\le 0.10$ Mpc/h. Tests for halos of masses ~10$^{12}$ M$_\odot$ to ~10$^{13}$ M$_\odot$ indicate that it is possible to ignore all details of the linear, scale dependent, bias function while obtaining robust constraints on cosmology. Our results provide a promising path forward to analyzes of galaxy surveys without the requirement of having to accurately model the details of galaxy biasing but by designing robust likelihoods for the inference.

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arXiv2021

The SIBELIUS Project: E Pluribus Unum

We introduce "Simulations Beyond The Local Universe" (SIBELIUS) that connect the Local Group to its cosmic environment. We show that introducing hierarchical small-scale perturbations to a density field constrained on large scales by observations provides an efficient way to explore the sample space of Local Group analogues. From more than 60 000 simulations, we identify a hierarchy of Local Group characteristics emanating from different scales: the total mass, orientation, orbital energy and the angular momentum are largely determined by modes above $λ$ = 1.6 comoving Mpc (cMpc) in the primordial density field. Smaller scale variations are mostly manifest as perturbations to the MW-M31 orbit, and we find that the observables commonly used to describe the Local Group -- the MW-M31 separation and radial velocity -- are transient and depend on specifying scales down to 0.2 cMpc in the primordial density field. We further find that the presence of M33/LMC analogues significantly affects the MW-M31 orbit and its sensitivity to small-scale perturbations. We construct initial conditions that lead to the formation of a Local Group whose primary observables precisely match the current observations.

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arXiv2021

Impacts of the physical data model on the forward inference of initial conditions from biased tracers

We investigate the impact of each ingredient in the employed physical data model on the Bayesian forward inference of initial conditions from biased tracers at the field level. Specifically, we use dark matter halos in a given cosmological simulation volume as tracers of the underlying matter density field. We study the effect of tracer density, grid resolution, gravity model, bias model and likelihood on the inferred initial conditions. We find that the cross-correlation coefficient between true and inferred phases reacts weakly to all ingredients above, and is well predicted by the theoretical expectation derived from a Gaussian model on a broad range of scales. The bias in the amplitude of the inferred initial conditions, on the other hand, depends strongly on the bias model and the likelihood. We conclude that the bias model and likelihood hold the key to an unbiased cosmological inference. Together they must keep the systematics -- which arise from the sub-grid physics that are marginalized over -- under control in order to obtain an unbiased inference.

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arXiv2021

Gravitation And the Universe from large Scale-Structures: The GAUSS mission concept

Today, thanks in particular to the results of the ESA Planck mission, the concordance cosmological model appears to be the most robust to describe the evolution and content of the Universe from its early to late times. It summarizes the evolution of matter, made mainly of dark matter, from the primordial fluctuations generated by inflation around $10^{-30}$ second after the Big-Bang to galaxies and clusters of galaxies, 13.8 billion years later, and the evolution of the expansion of space, with a relative slowdown in the matter-dominated era and, since a few billion years, an acceleration powered by dark energy. But we are far from knowing the pillars of this model which are inflation, dark matter and dark energy. Comprehending these fundamental questions requires a detailed mapping of our observable Universe over the whole of cosmic time. The relic radiation provides the starting point and galaxies draw the cosmic web. JAXA's LiteBIRD mission will map the beginning of our Universe with a crucial test for inflation (its primordial gravity waves), and the ESA Euclid mission will map the most recent half part, crucial for dark energy. The mission concept, described in this White Paper, GAUSS, aims at being a mission to fully map the cosmic web up to the reionization era, linking early and late evolution, to tackle and disentangle the crucial degeneracies persisting after the Euclid era between dark matter and inflation properties, dark energy, structure growth and gravitation at large scale.

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arXiv2021

Bayesian forward modelling of cosmic shear data

We present a Bayesian hierarchical modelling approach to infer the cosmic matter density field, and the lensing and the matter power spectra, from cosmic shear data. This method uses a physical model of cosmic structure formation to infer physically plausible cosmic structures, which accounts for the non-Gaussian features of the gravitationally evolved matter distribution and light-cone effects. We test and validate our framework with realistic simulated shear data, demonstrating that the method recovers the unbiased matter distribution and the correct lensing and matter power spectrum. While the cosmology is fixed in this test, and the method employs a prior power spectrum, we demonstrate that the lensing results are sensitive to the true power spectrum when this differs from the prior. In this case, the density field samples are generated with a power spectrum that deviates from the prior, and the method recovers the true lensing power spectrum. The method also recovers the matter power spectrum across the sky, but as currently implemented, it cannot determine the radial power since isotropy is not imposed. In summary, our method provides physically plausible inference of the dark matter distribution from cosmic shear data, allowing us to extract information beyond the two-point statistics and exploiting the full information content of the cosmological fields.

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arXiv2020

Velocity correction for Hubble constant measurements from standard sirens

Gravitational wave (GW) sources are an excellent probe of the luminosity distance and offer a novel measure of the Hubble constant, $H_0$. This estimation of $H_0$ from standard sirens requires an accurate estimation of the cosmological redshift of the host galaxy of the GW source, after correcting for its peculiar velocity. Absence of an accurate peculiar velocity correction affects both the precision and accuracy of the measurement of $H_0$, particularly for nearby sources. We propose a framework to incorporate such a peculiar velocity correction for GW sources. A first implementation of our method to the event GW170817 combined with the Very Large Baseline Interferometry (VLBI) observation leads to a revised value of $H_0= 68.3^{+ 4.6}_{-4.5}$ km/s/Mpc. While this revision is minor, it demonstrates that our method makes it possible for obtaining an unbiased and accurate measurements of $H_0$ at the precision required for the standard siren cosmology.

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arXiv2020

Precision cosmology with voids in the final BOSS data

We report novel cosmological constraints obtained from cosmic voids in the final BOSS DR12 dataset. They arise from the joint analysis of geometric and dynamic distortions of average void shapes (i.e., the stacked void-galaxy cross-correlation function) in redshift space. Our model uses tomographic deprojection to infer real-space void profiles and self-consistently accounts for the Alcock-Paczynski (AP) effect and redshift-space distortions (RSD) without any prior assumptions on cosmology or structure formation. It is derived from first physical principles and provides an extremely good description of the data at linear perturbation order. We validate this model with the help of mock catalogs and apply it to the final BOSS data to constrain the RSD and AP parameters $f/b$ and $D_AH/c$, where $f$ is the linear growth rate, $b$ the linear galaxy bias, $D_A$ the comoving angular diameter distance, $H$ the Hubble rate, and $c$ the speed of light. In addition, we include two nuisance parameters in our analysis to marginalize over potential systematics. We obtain $f/b=0.540\pm0.091$ and $D_AH/c=0.588\pm0.004$ from the full void sample at a mean redshift of $z=0.51$. In a flat $Λ$CDM cosmology, this implies $Ω_\mathrm{m}=0.312\pm0.020$ for the present-day matter density parameter. When we use additional information from the survey mocks to calibrate our model, these constraints improve to $f/b=0.347\pm0.023$, $D_AH/c=0.588\pm0.003$, and $Ω_\mathrm{m}=0.310\pm0.017$. However, we emphasize that the calibration depends on the specific model of cosmology and structure formation assumed in the mocks, so the calibrated results should be considered less robust. Nevertheless, our calibration-independent constraints are among the tightest of their kind to date, demonstrating the immense potential of using cosmic voids for cosmology in current and future data.

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arXiv2020

Unbiased Cosmology Inference from Biased Tracers using the EFT Likelihood

We present updates on the cosmology inference using the effective field theory (EFT) likelihood presented previously in Schmidt et al., 2018, Elsner et al., 2019 [1,2]. Specifically, we add a cutoff to the initial conditions that serve as starting point for the matter forward model. We show that this cutoff, which was not employed in any previous related work, is important to regularize loop integrals that otherwise involve small-scale, non-perturbative modes. We then present results on the inferred value of the linear power spectrum normalization $σ_{8}$ from rest-frame halo catalogs using both second- and third-order bias expansions, imposing uniform priors on all bias parameters. Due to the perfect bias-$σ_{8}$ degeneracy at linear order, constraints on $σ_{8}$ rely entirely on nonlinear information. The results show the expected convergence behavior when lowering the cutoff in wavenumber, $Λ$. When including modes up to $k \leq Λ= 0.1\,h\,{\rm Mpc}^{-1}$ in the second-order case, $σ_{8}$ is recovered to within $\lesssim 6\,\%$ for a range of halo masses and redshifts. The systematic bias shrinks to $4\,\%$ or less for the third-order bias expansion on the same range of scales. Together with additional evidence we provide, this shows that the residual mismatch in $σ_{8}$ can be attributed to higher-order bias contributions. We conclude that the EFT likelihood is able to infer unbiased cosmological constraints, within expected theoretical systematic errors, from physical biased tracers on quasilinear scales

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arXiv2020

Taking measurements of the kinematic Sunyaev-Zel'dovich effect forward: including uncertainties from velocity reconstruction with forward modeling

We measure the kinematic Sunyaev-Zel'dovich (kSZ) effect, imprinted by maxBCG clusters, on the Planck SMICA map of the Cosmic Microwave Background (CMB). Our measurement, for the first time, directly accounts for uncertainties in the velocity reconstruction step through the process of Bayesian forward modeling. We show that this often neglected uncertainty budget typically increases the final uncertainty on the measured kSZ signal amplitude by $\simeq15\%$ at cluster scale. We observe evidence for the kSZ effect, at a significance of $\simeq2σ$. Our analysis, when applied to future higher-resolution CMB data, together with minor improvements in map-filtering and signal-modeling methods, should yield both significant and unbiased measurements of the kSZ signal, which can then be used to probe and constrain baryonic content of galaxy clusters and galaxy groups.

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arXiv2020

Cosmic flows in the nearby Universe: new peculiar velocities from SNe and cosmological constraints

The peculiar velocity field offers a unique way to probe dark matter density field on large scales at low redshifts. In this work, we have compiled a new sample of 465 peculiar velocities from low redshift $(z < 0.067)$ Type Ia supernovae. We compare the reconstructed velocity field derived from the 2M++ galaxy redshift compilation to the supernovae, the SFI++ and the 2MTF Tully-Fisher distance catalogues. We used a forward method to jointly infer the distances and the velocities of distance indicators by comparing the observations to the reconstruction. Comparison of the reconstructed peculiar velocity fields to observations allows us to infer the cosmological parameter combination $fσ_8$, and the bulk flow velocity arising from outside the survey volume. The residual bulk flow arising from outside the 2M++ volume is inferred to be $171^{+11}_{-11}$ km s$^{-1}$ in the direction $l=301^{\circ} \pm 4^{\circ}$ and $b=0^{\circ} \pm 3^{\circ}$. We obtain $fσ_{8} = 0.400 \pm 0.017$, equivalent to $S_8 \approx σ_8(Ω_m/0.3)^{0.55}=0.776\pm0.033$, which corresponds to an approximately $ 4\%\,$ statistical uncertainty on the value of $fσ_8$. Our inferred value is consistent with other low redshift results in the literature.

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arXiv2020

Peculiar velocities in the local Universe: comparison of different models and the implications for $H_0$ and dark matter

When measuring the value of the Hubble parameter, $H_0$, it is necessary to know the recession velocity free of the effects of peculiar velocities. In this work, we study different models of peculiar velocity in the local Universe. In particular, we compare models based on density reconstruction from galaxy redshift surveys and kernel smoothing of peculiar velocity data. The velocity field from the density reconstruction is obtained using the 2M++ galaxy redshift compilation, which is compared to two adaptive kernel-smoothed velocity fields: the first obtained from the 6dF Fundamental Plane sample and the other using a Tully-Fisher catalogue obtained by combining SFI++ and 2MTF. We highlight that smoothed velocity fields should be rescaled to obtain unbiased velocity estimates. Comparing the predictions of these models to the observations from a few test sets of peculiar velocity data, obtained from the Second Amendment Supernovae catalogue and the Tully-Fisher catalogues, we find that 2M++ reconstruction provides a better model of the peculiar velocity in the local Universe than the kernel-smoothed peculiar velocity models. We study the impact of peculiar velocities on the measurement of $H_0$ from gravitational waves and megamasers. In doing so, we introduce a probabilistic framework to marginalize over the peculiar velocity corrections along the line-of-sight. For the megamasers, we find $H_0 = 69^{+2.9}_{-2.8}$ km s^{-1} Mpc^{-1} using the 2M++ velocity field. We also study the peculiar velocity of the the galaxy NGC1052-DF2, concluding that a short $\sim$ 13 Mpc distance is not a likely explanation of the anomalously low dark matter fraction of that galaxy.

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arXiv2020

A hierarchical field-level inference approach to reconstruction from sparse Lyman-$α$ forest data

We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-$α$ forest. Using a Bayesian forward modelling approach, we focus on extending the dynamical model to a fully self-consistent hierarchical field-level prediction of redshift-space quasar absorption sightlines. Our field-level approach rests on a recently developed semiclassical analogue to Lagrangian perturbation theory (LPT), which improves over noise problems and interpolation requirements of LPT. It furthermore allows for a manifestly conservative mapping of the optical depth to redshift space. In addition, this new dynamical model naturally introduces a coarse-graining scale, which we exploited to accelerate the Markov chain Monte-Carlo (MCMC) sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we were able to allow it to first converge on large spatial scales before the sampler became sensitive to the increasingly larger space of smaller scales. We demonstrate the advantages, in terms of speed and noise properties, of this field-level approach over using LPT as a forward model, and, using mock data, we validated its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.

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